Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations24847481
Missing cells3611267
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 GiB
Average record size in memory908.3 B

Variable types

Text9
DateTime1
Numeric12
Unsupported2

Alerts

Store Location has 2450072 (9.9%) missing valuesMissing
County Number has 714638 (2.9%) missing valuesMissing
Bottle Volume (ml) is highly skewed (γ1 = 207.8010243)Skewed
State Bottle Cost is highly skewed (γ1 = 183.009033)Skewed
State Bottle Retail is highly skewed (γ1 = 183.0325335)Skewed
Bottles Sold is highly skewed (γ1 = 65.3044445)Skewed
Sale (Dollars) is highly skewed (γ1 = 88.29524725)Skewed
Volume Sold (Liters) is highly skewed (γ1 = 65.33839987)Skewed
Volume Sold (Gallons) is highly skewed (γ1 = 65.33652904)Skewed
Invoice/Item Number has unique valuesUnique
Zip Code is an unsupported type, check if it needs cleaning or further analysisUnsupported
Item Number is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-08-09 23:59:51.990494
Analysis finished2024-08-10 00:24:47.211526
Duration24 minutes and 55.22 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Invoice/Item Number
Text

UNIQUE 

Distinct24847481
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 GiB
2024-08-10T00:25:11.358044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length16
Median length15
Mean length13.807005
Min length9

Characters and Unicode

Total characters343069303
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24847481 ?
Unique (%)100.0%

Sample

1st rowINV-16589800035
2nd rowINV-16669000029
3rd rowINV-16624000064
4th rowINV-16610400055
5th rowINV-16575300022
ValueCountFrequency (%)
inv-16639800026 1
 
< 0.1%
inv-16619600095 1
 
< 0.1%
inv-16589800035 1
 
< 0.1%
inv-16669000029 1
 
< 0.1%
inv-16624000064 1
 
< 0.1%
inv-16610400055 1
 
< 0.1%
inv-16575300022 1
 
< 0.1%
inv-16623300001 1
 
< 0.1%
inv-16615400044 1
 
< 0.1%
inv-16676400069 1
 
< 0.1%
Other values (24847471) 24847471
> 99.9%
2024-08-10T00:25:35.792046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 98476952
28.7%
1 29326157
 
8.5%
2 25533278
 
7.4%
3 22064596
 
6.4%
4 19126161
 
5.6%
5 16856716
 
4.9%
6 16030662
 
4.7%
7 15555848
 
4.5%
8 15283674
 
4.5%
9 15069782
 
4.4%
Other values (6) 69745477
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 343069303
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 98476952
28.7%
1 29326157
 
8.5%
2 25533278
 
7.4%
3 22064596
 
6.4%
4 19126161
 
5.6%
5 16856716
 
4.9%
6 16030662
 
4.7%
7 15555848
 
4.5%
8 15283674
 
4.5%
9 15069782
 
4.4%
Other values (6) 69745477
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 343069303
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 98476952
28.7%
1 29326157
 
8.5%
2 25533278
 
7.4%
3 22064596
 
6.4%
4 19126161
 
5.6%
5 16856716
 
4.9%
6 16030662
 
4.7%
7 15555848
 
4.5%
8 15283674
 
4.5%
9 15069782
 
4.4%
Other values (6) 69745477
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 343069303
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 98476952
28.7%
1 29326157
 
8.5%
2 25533278
 
7.4%
3 22064596
 
6.4%
4 19126161
 
5.6%
5 16856716
 
4.9%
6 16030662
 
4.7%
7 15555848
 
4.5%
8 15283674
 
4.5%
9 15069782
 
4.4%
Other values (6) 69745477
20.3%

Date
Date

Distinct2730
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size189.6 MiB
Minimum2012-01-03 00:00:00
Maximum2022-09-30 00:00:00
2024-08-10T00:25:35.979674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:25:36.150354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Store Number
Real number (ℝ)

Distinct2807
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3786.0345
Minimum2106
Maximum10057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size189.6 MiB
2024-08-10T00:25:36.336578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2106
5-th percentile2506
Q12619
median3829
Q34642
95-th percentile5600
Maximum10057
Range7951
Interquartile range (IQR)2023

Descriptive statistics

Standard deviation1104.2915
Coefficient of variation (CV)0.29167497
Kurtosis-0.59070345
Mean3786.0345
Median Absolute Deviation (MAD)1172
Skewness0.34019531
Sum9.4073421 × 1010
Variance1219459.8
MonotonicityNot monotonic
2024-08-10T00:25:36.515419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2633 205995
 
0.8%
4829 173686
 
0.7%
2190 163078
 
0.7%
2512 139260
 
0.6%
2572 131220
 
0.5%
2603 128754
 
0.5%
2515 124798
 
0.5%
2614 124479
 
0.5%
2647 120373
 
0.5%
2648 119680
 
0.5%
Other values (2797) 23416158
94.2%
ValueCountFrequency (%)
2106 46159
 
0.2%
2113 11419
 
< 0.1%
2130 39072
 
0.2%
2132 535
 
< 0.1%
2152 6649
 
< 0.1%
2161 566
 
< 0.1%
2178 23290
 
0.1%
2190 163078
0.7%
2191 60926
 
0.2%
2200 39148
 
0.2%
ValueCountFrequency (%)
10057 144
 
< 0.1%
10053 87
 
< 0.1%
10051 121
 
< 0.1%
10046 84
 
< 0.1%
10045 260
 
< 0.1%
10044 35
 
< 0.1%
10042 162
 
< 0.1%
10040 56
 
< 0.1%
10039 1610
< 0.1%
10037 694
< 0.1%
Distinct2972
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 GiB
2024-08-10T00:25:36.895855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length58
Median length42
Mean length26.158078
Min length4

Characters and Unicode

Total characters649962354
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowHy-Vee Food Store / Oskaloosa
2nd rowStewart Road Fast Break
3rd rowHy-Vee Food Store / Carroll
4th rowFareway Stores #909 / Ankeny
5th rowBig G Food Store
ValueCountFrequency (%)
19402342
 
16.1%
hy-vee 8172944
 
6.8%
store 5547356
 
4.6%
food 4809433
 
4.0%
and 3561175
 
3.0%
liquor 3178494
 
2.6%
spirits 2590565
 
2.1%
wine 2188822
 
1.8%
city 2006216
 
1.7%
cedar 1472216
 
1.2%
Other values (2548) 67614587
56.1%
2024-08-10T00:25:37.439215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
96494134
 
14.8%
e 56030548
 
8.6%
o 44093308
 
6.8%
r 36054932
 
5.5%
a 31534722
 
4.9%
t 28479887
 
4.4%
i 27187115
 
4.2%
n 23026225
 
3.5%
s 21045699
 
3.2%
/ 17502571
 
2.7%
Other values (65) 268513213
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 649962354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
96494134
 
14.8%
e 56030548
 
8.6%
o 44093308
 
6.8%
r 36054932
 
5.5%
a 31534722
 
4.9%
t 28479887
 
4.4%
i 27187115
 
4.2%
n 23026225
 
3.5%
s 21045699
 
3.2%
/ 17502571
 
2.7%
Other values (65) 268513213
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 649962354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
96494134
 
14.8%
e 56030548
 
8.6%
o 44093308
 
6.8%
r 36054932
 
5.5%
a 31534722
 
4.9%
t 28479887
 
4.4%
i 27187115
 
4.2%
n 23026225
 
3.5%
s 21045699
 
3.2%
/ 17502571
 
2.7%
Other values (65) 268513213
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 649962354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
96494134
 
14.8%
e 56030548
 
8.6%
o 44093308
 
6.8%
r 36054932
 
5.5%
a 31534722
 
4.9%
t 28479887
 
4.4%
i 27187115
 
4.2%
n 23026225
 
3.5%
s 21045699
 
3.2%
/ 17502571
 
2.7%
Other values (65) 268513213
41.3%
Distinct4134
Distinct (%)< 0.1%
Missing81913
Missing (%)0.3%
Memory size1.7 GiB
2024-08-10T00:25:37.830033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length55
Median length32
Mean length15.851496
Min length8

Characters and Unicode

Total characters392571292
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row110 S D St
2nd row2418 Stewart Rd
3rd row905 US Highway 30 West
4th row109 SE Oralabor Rd
5th rowPo Box 261 310 W Dillon
ValueCountFrequency (%)
st 8593247
 
9.7%
ave 5173259
 
5.8%
e 2034123
 
2.3%
rd 2032702
 
2.3%
w 1811805
 
2.0%
s 1496868
 
1.7%
n 1483308
 
1.7%
dr 1467716
 
1.7%
se 1231645
 
1.4%
main 1210111
 
1.4%
Other values (1894) 62112033
70.1%
2024-08-10T00:25:38.363293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66158967
 
16.9%
1 23363309
 
6.0%
0 18649626
 
4.8%
S 18056666
 
4.6%
t 14873854
 
3.8%
2 13310956
 
3.4%
E 12815806
 
3.3%
e 12453780
 
3.2%
A 11632097
 
3.0%
5 9692813
 
2.5%
Other values (60) 191563418
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 392571292
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
66158967
 
16.9%
1 23363309
 
6.0%
0 18649626
 
4.8%
S 18056666
 
4.6%
t 14873854
 
3.8%
2 13310956
 
3.4%
E 12815806
 
3.3%
e 12453780
 
3.2%
A 11632097
 
3.0%
5 9692813
 
2.5%
Other values (60) 191563418
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 392571292
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
66158967
 
16.9%
1 23363309
 
6.0%
0 18649626
 
4.8%
S 18056666
 
4.6%
t 14873854
 
3.8%
2 13310956
 
3.4%
E 12815806
 
3.3%
e 12453780
 
3.2%
A 11632097
 
3.0%
5 9692813
 
2.5%
Other values (60) 191563418
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 392571292
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
66158967
 
16.9%
1 23363309
 
6.0%
0 18649626
 
4.8%
S 18056666
 
4.6%
t 14873854
 
3.8%
2 13310956
 
3.4%
E 12815806
 
3.3%
e 12453780
 
3.2%
A 11632097
 
3.0%
5 9692813
 
2.5%
Other values (60) 191563418
48.8%

City
Text

Distinct859
Distinct (%)< 0.1%
Missing81912
Missing (%)0.3%
Memory size1.5 GiB
2024-08-10T00:25:38.806376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length15
Mean length9.1979476
Min length3

Characters and Unicode

Total characters227792406
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOskaloosa
2nd rowMuscatine
3rd rowCarroll
4th rowAnkeny
5th rowMarengo
ValueCountFrequency (%)
des 2845492
 
7.9%
moines 2845492
 
7.9%
city 2202300
 
6.1%
cedar 2110848
 
5.8%
rapids 1681705
 
4.7%
davenport 1066185
 
3.0%
west 865098
 
2.4%
sioux 791761
 
2.2%
waterloo 780243
 
2.2%
iowa 763989
 
2.1%
Other values (482) 20176657
55.8%
2024-08-10T00:25:39.395495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14916536
 
6.5%
11364201
 
5.0%
o 11147155
 
4.9%
a 10607823
 
4.7%
n 10011793
 
4.4%
s 9316873
 
4.1%
E 9007701
 
4.0%
i 8963399
 
3.9%
A 8122206
 
3.6%
C 7904330
 
3.5%
Other values (46) 126430389
55.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 227792406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14916536
 
6.5%
11364201
 
5.0%
o 11147155
 
4.9%
a 10607823
 
4.7%
n 10011793
 
4.4%
s 9316873
 
4.1%
E 9007701
 
4.0%
i 8963399
 
3.9%
A 8122206
 
3.6%
C 7904330
 
3.5%
Other values (46) 126430389
55.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 227792406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14916536
 
6.5%
11364201
 
5.0%
o 11147155
 
4.9%
a 10607823
 
4.7%
n 10011793
 
4.4%
s 9316873
 
4.1%
E 9007701
 
4.0%
i 8963399
 
3.9%
A 8122206
 
3.6%
C 7904330
 
3.5%
Other values (46) 126430389
55.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 227792406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14916536
 
6.5%
11364201
 
5.0%
o 11147155
 
4.9%
a 10607823
 
4.7%
n 10011793
 
4.4%
s 9316873
 
4.1%
E 9007701
 
4.0%
i 8963399
 
3.9%
A 8122206
 
3.6%
C 7904330
 
3.5%
Other values (46) 126430389
55.5%

Zip Code
Unsupported

REJECTED  UNSUPPORTED 

Missing81957
Missing (%)0.3%
Memory size1.1 GiB

Store Location
Text

MISSING 

Distinct3203
Distinct (%)< 0.1%
Missing2450072
Missing (%)9.9%
Memory size1.9 GiB
2024-08-10T00:25:39.729731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length45
Median length28
Mean length28.848594
Min length23

Characters and Unicode

Total characters646133761
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowPOINT (-92.649764 41.295218)
2nd rowPOINT (-91.07265200000002 41.390911)
3rd rowPOINT (-94.853591 42.064155)
4th rowPOINT (-93.59949600000002 41.702811)
5th rowPOINT (-94.377667 42.026833)
ValueCountFrequency (%)
point 22397409
33.3%
93.596754 189532
 
0.3%
41.554101 189532
 
0.3%
93.613739 155441
 
0.2%
41.60572 155441
 
0.2%
93.619787 151647
 
0.2%
41.60566 151647
 
0.2%
42.512789 131216
 
0.2%
92.435236 131216
 
0.2%
91.53046300000001 131200
 
0.2%
Other values (5932) 43407946
64.6%
2024-08-10T00:25:40.206059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 50476897
 
7.8%
0 47062048
 
7.3%
9 46185792
 
7.1%
1 45904070
 
7.1%
. 44794818
 
6.9%
44794818
 
6.9%
2 35257191
 
5.5%
3 34526189
 
5.3%
5 33797112
 
5.2%
6 31973632
 
4.9%
Other values (10) 231361194
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 646133761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 50476897
 
7.8%
0 47062048
 
7.3%
9 46185792
 
7.1%
1 45904070
 
7.1%
. 44794818
 
6.9%
44794818
 
6.9%
2 35257191
 
5.5%
3 34526189
 
5.3%
5 33797112
 
5.2%
6 31973632
 
4.9%
Other values (10) 231361194
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 646133761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 50476897
 
7.8%
0 47062048
 
7.3%
9 46185792
 
7.1%
1 45904070
 
7.1%
. 44794818
 
6.9%
44794818
 
6.9%
2 35257191
 
5.5%
3 34526189
 
5.3%
5 33797112
 
5.2%
6 31973632
 
4.9%
Other values (10) 231361194
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 646133761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 50476897
 
7.8%
0 47062048
 
7.3%
9 46185792
 
7.1%
1 45904070
 
7.1%
. 44794818
 
6.9%
44794818
 
6.9%
2 35257191
 
5.5%
3 34526189
 
5.3%
5 33797112
 
5.2%
6 31973632
 
4.9%
Other values (10) 231361194
35.8%

County Number
Real number (ℝ)

MISSING 

Distinct99
Distinct (%)< 0.1%
Missing714638
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean57.259133
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size189.6 MiB
2024-08-10T00:25:40.386630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q131
median62
Q377
95-th percentile94
Maximum99
Range98
Interquartile range (IQR)46

Descriptive statistics

Standard deviation27.287104
Coefficient of variation (CV)0.47655462
Kurtosis-0.94931404
Mean57.259133
Median Absolute Deviation (MAD)17
Skewness-0.53944166
Sum1.3818257 × 109
Variance744.58607
MonotonicityNot monotonic
2024-08-10T00:25:40.545930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 4459618
17.9%
57 2013416
 
8.1%
82 1472224
 
5.9%
7 1353303
 
5.4%
52 1210494
 
4.9%
78 796208
 
3.2%
85 771807
 
3.1%
97 755174
 
3.0%
31 701537
 
2.8%
17 533730
 
2.1%
Other values (89) 10065332
40.5%
(Missing) 714638
 
2.9%
ValueCountFrequency (%)
1 50009
 
0.2%
2 19865
 
0.1%
3 99368
 
0.4%
4 86089
 
0.3%
5 26544
 
0.1%
6 114142
 
0.5%
7 1353303
5.4%
8 173668
 
0.7%
9 187794
 
0.8%
10 135565
 
0.5%
ValueCountFrequency (%)
99 69415
 
0.3%
98 34253
 
0.1%
97 755174
3.0%
96 122996
 
0.5%
95 83972
 
0.3%
94 274118
 
1.1%
93 18725
 
0.1%
92 147811
 
0.6%
91 222011
 
0.9%
90 278134
 
1.1%

County
Text

Distinct201
Distinct (%)< 0.1%
Missing158715
Missing (%)0.6%
Memory size1.5 GiB
2024-08-10T00:25:40.869200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length10
Mean length6.3615352
Min length3

Characters and Unicode

Total characters157058455
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAHASKA
2nd rowMUSCATINE
3rd rowCARROLL
4th rowPOLK
5th rowIOWA
ValueCountFrequency (%)
polk 4568617
 
16.7%
linn 2059315
 
7.5%
scott 1502782
 
5.5%
black 1383941
 
5.1%
hawk 1383941
 
5.1%
johnson 1236666
 
4.5%
story 788339
 
2.9%
woodbury 771923
 
2.8%
dubuque 715855
 
2.6%
cerro 544520
 
2.0%
Other values (101) 12370083
45.3%
2024-08-10T00:25:41.337236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 10932567
 
7.0%
L 8394015
 
5.3%
o 8103942
 
5.2%
N 7764016
 
4.9%
A 7578533
 
4.8%
P 6332829
 
4.0%
S 6138724
 
3.9%
T 5925866
 
3.8%
n 5369048
 
3.4%
K 5216964
 
3.3%
Other values (41) 85301951
54.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 157058455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 10932567
 
7.0%
L 8394015
 
5.3%
o 8103942
 
5.2%
N 7764016
 
4.9%
A 7578533
 
4.8%
P 6332829
 
4.0%
S 6138724
 
3.9%
T 5925866
 
3.8%
n 5369048
 
3.4%
K 5216964
 
3.3%
Other values (41) 85301951
54.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 157058455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 10932567
 
7.0%
L 8394015
 
5.3%
o 8103942
 
5.2%
N 7764016
 
4.9%
A 7578533
 
4.8%
P 6332829
 
4.0%
S 6138724
 
3.9%
T 5925866
 
3.8%
n 5369048
 
3.4%
K 5216964
 
3.3%
Other values (41) 85301951
54.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 157058455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 10932567
 
7.0%
L 8394015
 
5.3%
o 8103942
 
5.2%
N 7764016
 
4.9%
A 7578533
 
4.8%
P 6332829
 
4.0%
S 6138724
 
3.9%
T 5925866
 
3.8%
n 5369048
 
3.4%
K 5216964
 
3.3%
Other values (41) 85301951
54.3%

Category
Real number (ℝ)

Distinct114
Distinct (%)< 0.1%
Missing16974
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1049080.2
Minimum101220
Maximum1901200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size189.6 MiB
2024-08-10T00:25:41.510373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum101220
5-th percentile1011200
Q11012200
median1031200
Q31062400
95-th percentile1082000
Maximum1901200
Range1799980
Interquartile range (IQR)50200

Descriptive statistics

Standard deviation78245.098
Coefficient of variation (CV)0.074584478
Kurtosis64.802301
Mean1049080.2
Median Absolute Deviation (MAD)19810
Skewness7.6585159
Sum2.6049193 × 1013
Variance6.1222953 × 109
MonotonicityNot monotonic
2024-08-10T00:25:41.678288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1012100 2376643
 
9.6%
1031100 2337813
 
9.4%
1011200 1537217
 
6.2%
1031080 1265974
 
5.1%
1031200 1236935
 
5.0%
1081600 1095653
 
4.4%
1011100 1070908
 
4.3%
1022100 758381
 
3.1%
1062200 739758
 
3.0%
1062400 704020
 
2.8%
Other values (104) 11707205
47.1%
ValueCountFrequency (%)
101220 6
 
< 0.1%
1011000 1
 
< 0.1%
1011100 1070908
4.3%
1011200 1537217
6.2%
1011250 9268
 
< 0.1%
1011300 267674
 
1.1%
1011400 417493
 
1.7%
1011500 77896
 
0.3%
1011600 110947
 
0.4%
1011700 10223
 
< 0.1%
ValueCountFrequency (%)
1901200 28045
 
0.1%
1901100 63
 
< 0.1%
1901000 423
 
< 0.1%
1900000 1
 
< 0.1%
1701200 48
 
< 0.1%
1701100 247903
1.0%
1700000 17944
 
0.1%
1501100 32
 
< 0.1%
1101100 24351
 
0.1%
1092100 20473
 
0.1%
Distinct136
Distinct (%)< 0.1%
Missing25040
Missing (%)0.1%
Memory size1.7 GiB
2024-08-10T00:25:42.009177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length36
Median length34
Mean length17.324517
Min length6

Characters and Unicode

Total characters430036803
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowStraight Rye Whiskies
2nd rowImported Schnapps
3rd rowSingle Barrel Bourbon Whiskies
4th rowTemporary & Specialty Packages
5th rowStraight Rye Whiskies
ValueCountFrequency (%)
whiskies 6559168
 
10.8%
american 5592758
 
9.2%
vodka 3557828
 
5.8%
imported 3191448
 
5.2%
rum 2780252
 
4.6%
vodkas 2724570
 
4.5%
canadian 2376643
 
3.9%
flavored 2256480
 
3.7%
1999419
 
3.3%
straight 1707281
 
2.8%
Other values (100) 28162235
46.2%
2024-08-10T00:25:42.516204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36107834
 
8.4%
i 23566038
 
5.5%
e 22403455
 
5.2%
a 20761269
 
4.8%
I 18042114
 
4.2%
A 17967391
 
4.2%
s 16752032
 
3.9%
S 16400073
 
3.8%
r 15293605
 
3.6%
R 14051712
 
3.3%
Other values (48) 228691280
53.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 430036803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
36107834
 
8.4%
i 23566038
 
5.5%
e 22403455
 
5.2%
a 20761269
 
4.8%
I 18042114
 
4.2%
A 17967391
 
4.2%
s 16752032
 
3.9%
S 16400073
 
3.8%
r 15293605
 
3.6%
R 14051712
 
3.3%
Other values (48) 228691280
53.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 430036803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
36107834
 
8.4%
i 23566038
 
5.5%
e 22403455
 
5.2%
a 20761269
 
4.8%
I 18042114
 
4.2%
A 17967391
 
4.2%
s 16752032
 
3.9%
S 16400073
 
3.8%
r 15293605
 
3.6%
R 14051712
 
3.3%
Other values (48) 228691280
53.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 430036803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
36107834
 
8.4%
i 23566038
 
5.5%
e 22403455
 
5.2%
a 20761269
 
4.8%
I 18042114
 
4.2%
A 17967391
 
4.2%
s 16752032
 
3.9%
S 16400073
 
3.8%
r 15293605
 
3.6%
R 14051712
 
3.3%
Other values (48) 228691280
53.2%

Vendor Number
Real number (ℝ)

Distinct428
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean266.71765
Minimum10
Maximum987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size189.6 MiB
2024-08-10T00:25:42.695426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile55
Q1115
median260
Q3391
95-th percentile434
Maximum987
Range977
Interquartile range (IQR)276

Descriptive statistics

Standard deviation141.89777
Coefficient of variation (CV)0.53201494
Kurtosis-0.034845742
Mean266.71765
Median Absolute Deviation (MAD)135
Skewness0.022030204
Sum6.6272593 × 109
Variance20134.978
MonotonicityNot monotonic
2024-08-10T00:25:42.869026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
260 4127854
16.6%
65 2307531
 
9.3%
434 2206741
 
8.9%
421 1918825
 
7.7%
370 1474144
 
5.9%
259 1278543
 
5.1%
35 1106048
 
4.5%
115 1044480
 
4.2%
55 1040215
 
4.2%
395 960804
 
3.9%
Other values (418) 7382287
29.7%
ValueCountFrequency (%)
10 1003
 
< 0.1%
14 1
 
< 0.1%
27 3
 
< 0.1%
33 23
 
< 0.1%
35 1106048
4.5%
51 1
 
< 0.1%
55 1040215
4.2%
61 11
 
< 0.1%
65 2307531
9.3%
68 1
 
< 0.1%
ValueCountFrequency (%)
987 31
 
< 0.1%
978 11891
< 0.1%
977 598
 
< 0.1%
971 15858
0.1%
969 1565
 
< 0.1%
962 3029
 
< 0.1%
892 5
 
< 0.1%
890 1
 
< 0.1%
888 401
 
< 0.1%
886 1133
 
< 0.1%
Distinct585
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size1.7 GiB
2024-08-10T00:25:43.201804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length67
Median length57
Mean length17.958747
Min length7

Characters and Unicode

Total characters446229506
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)< 0.1%

Sample

1st rowInfinium Spirits
2nd rowSAZERAC COMPANY INC
3rd rowHeaven Hill Brands
4th rowDIAGEO AMERICAS
5th rowInfinium Spirits
ValueCountFrequency (%)
inc 6432419
 
9.3%
brands 4128875
 
5.9%
americas 4128257
 
5.9%
diageo 4127854
 
5.9%
company 3495628
 
5.0%
sazerac 2959040
 
4.3%
jim 2307531
 
3.3%
beam 2307531
 
3.3%
usa 2009414
 
2.9%
pernod 1474144
 
2.1%
Other values (715) 36031084
51.9%
2024-08-10T00:25:43.726587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46468880
 
10.4%
A 24969779
 
5.6%
a 22762692
 
5.1%
i 22639415
 
5.1%
C 19673973
 
4.4%
r 18469298
 
4.1%
n 18328273
 
4.1%
e 17600154
 
3.9%
I 16960889
 
3.8%
o 16726088
 
3.7%
Other values (61) 221630065
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 446229506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
46468880
 
10.4%
A 24969779
 
5.6%
a 22762692
 
5.1%
i 22639415
 
5.1%
C 19673973
 
4.4%
r 18469298
 
4.1%
n 18328273
 
4.1%
e 17600154
 
3.9%
I 16960889
 
3.8%
o 16726088
 
3.7%
Other values (61) 221630065
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 446229506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
46468880
 
10.4%
A 24969779
 
5.6%
a 22762692
 
5.1%
i 22639415
 
5.1%
C 19673973
 
4.4%
r 18469298
 
4.1%
n 18328273
 
4.1%
e 17600154
 
3.9%
I 16960889
 
3.8%
o 16726088
 
3.7%
Other values (61) 221630065
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 446229506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
46468880
 
10.4%
A 24969779
 
5.6%
a 22762692
 
5.1%
i 22639415
 
5.1%
C 19673973
 
4.4%
r 18469298
 
4.1%
n 18328273
 
4.1%
e 17600154
 
3.9%
I 16960889
 
3.8%
o 16726088
 
3.7%
Other values (61) 221630065
49.7%

Item Number
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size854.7 MiB
Distinct10911
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 GiB
2024-08-10T00:25:44.110925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length63
Median length52
Mean length20.979921
Min length2

Characters and Unicode

Total characters521298190
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1137 ?
Unique (%)< 0.1%

Sample

1st rowTempleton 4YR Rye
2nd rowDr McGillicuddys Cherry
3rd rowEvan Williams Vintage
4th rowSmirnoff Peppermint Twist
5th rowTempleton Rye Rare Cask Strength
ValueCountFrequency (%)
vodka 4457394
 
5.4%
mini 1995067
 
2.4%
rum 1794303
 
2.2%
black 1688907
 
2.1%
whiskey 1375358
 
1.7%
spiced 1197714
 
1.5%
crown 1126861
 
1.4%
pet 1086395
 
1.3%
velvet 971338
 
1.2%
liqueur 880339
 
1.1%
Other values (5103) 65447005
79.8%
2024-08-10T00:25:44.683968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57181604
 
11.0%
a 42706416
 
8.2%
e 41239590
 
7.9%
i 31446331
 
6.0%
r 31154241
 
6.0%
o 27157260
 
5.2%
n 26456014
 
5.1%
l 21490373
 
4.1%
s 16448032
 
3.2%
d 15303158
 
2.9%
Other values (77) 210715171
40.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 521298190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
57181604
 
11.0%
a 42706416
 
8.2%
e 41239590
 
7.9%
i 31446331
 
6.0%
r 31154241
 
6.0%
o 27157260
 
5.2%
n 26456014
 
5.1%
l 21490373
 
4.1%
s 16448032
 
3.2%
d 15303158
 
2.9%
Other values (77) 210715171
40.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 521298190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
57181604
 
11.0%
a 42706416
 
8.2%
e 41239590
 
7.9%
i 31446331
 
6.0%
r 31154241
 
6.0%
o 27157260
 
5.2%
n 26456014
 
5.1%
l 21490373
 
4.1%
s 16448032
 
3.2%
d 15303158
 
2.9%
Other values (77) 210715171
40.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 521298190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
57181604
 
11.0%
a 42706416
 
8.2%
e 41239590
 
7.9%
i 31446331
 
6.0%
r 31154241
 
6.0%
o 27157260
 
5.2%
n 26456014
 
5.1%
l 21490373
 
4.1%
s 16448032
 
3.2%
d 15303158
 
2.9%
Other values (77) 210715171
40.4%

Pack
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.204653
Minimum1
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size189.6 MiB
2024-08-10T00:25:44.847290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q16
median12
Q312
95-th percentile24
Maximum336
Range335
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.7094335
Coefficient of variation (CV)0.63167985
Kurtosis21.924377
Mean12.204653
Median Absolute Deviation (MAD)0
Skewness3.122434
Sum3.0325489 × 108
Variance59.435364
MonotonicityNot monotonic
2024-08-10T00:25:44.982137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
12 14108264
56.8%
6 6929906
27.9%
24 2251084
 
9.1%
48 625906
 
2.5%
10 490828
 
2.0%
1 193894
 
0.8%
8 76740
 
0.3%
5 57258
 
0.2%
44 41927
 
0.2%
4 35532
 
0.1%
Other values (18) 36142
 
0.1%
ValueCountFrequency (%)
1 193894
 
0.8%
2 645
 
< 0.1%
3 27025
 
0.1%
4 35532
 
0.1%
5 57258
 
0.2%
6 6929906
27.9%
8 76740
 
0.3%
9 2050
 
< 0.1%
10 490828
 
2.0%
12 14108264
56.8%
ValueCountFrequency (%)
336 13
 
< 0.1%
312 40
 
< 0.1%
288 101
 
< 0.1%
160 4
 
< 0.1%
120 5
 
< 0.1%
96 1
 
< 0.1%
80 9
 
< 0.1%
60 1265
 
< 0.1%
48 625906
2.5%
44 41927
 
0.2%

Bottle Volume (ml)
Real number (ℝ)

SKEWED 

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean892.22323
Minimum0
Maximum378000
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size189.6 MiB
2024-08-10T00:25:45.146204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200
Q1750
median750
Q31000
95-th percentile1750
Maximum378000
Range378000
Interquartile range (IQR)250

Descriptive statistics

Standard deviation639.17426
Coefficient of variation (CV)0.7163838
Kurtosis119468.83
Mean892.22323
Median Absolute Deviation (MAD)250
Skewness207.80102
Sum2.21695 × 1010
Variance408543.74
MonotonicityNot monotonic
2024-08-10T00:25:45.304698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750 11153324
44.9%
1750 4903124
19.7%
1000 3105652
 
12.5%
375 2544998
 
10.2%
200 879922
 
3.5%
50 865857
 
3.5%
500 730575
 
2.9%
600 221220
 
0.9%
100 175941
 
0.7%
3000 95623
 
0.4%
Other values (48) 171245
 
0.7%
ValueCountFrequency (%)
0 10
 
< 0.1%
12 1
 
< 0.1%
15 1
 
< 0.1%
20 1920
 
< 0.1%
25 253
 
< 0.1%
50 865857
3.5%
100 175941
 
0.7%
150 793
 
< 0.1%
175 54
 
< 0.1%
200 879922
3.5%
ValueCountFrequency (%)
378000 24
 
< 0.1%
225000 1
 
< 0.1%
189000 2
 
< 0.1%
180000 2
 
< 0.1%
140000 8
 
< 0.1%
31500 24
 
< 0.1%
9000 3
 
< 0.1%
7580 1
 
< 0.1%
6000 1299
< 0.1%
5250 194
 
< 0.1%

State Bottle Cost
Real number (ℝ)

SKEWED 

Distinct3377
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.265363
Minimum0
Maximum7680
Zeros3084
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size189.6 MiB
2024-08-10T00:25:45.484170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.96
Q15.52
median8.25
Q312.5
95-th percentile23.48
Maximum7680
Range7680
Interquartile range (IQR)6.98

Descriptive statistics

Standard deviation11.043744
Coefficient of variation (CV)1.075826
Kurtosis83951.321
Mean10.265363
Median Absolute Deviation (MAD)3.24
Skewness183.00903
Sum2.5506831 × 108
Variance121.96429
MonotonicityNot monotonic
2024-08-10T00:25:45.662972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.25 493597
 
2.0%
7.5 442179
 
1.8%
10 422000
 
1.7%
6.5 411121
 
1.7%
5 332540
 
1.3%
11 268716
 
1.1%
8 257565
 
1.0%
7 255836
 
1.0%
3.5 254111
 
1.0%
6 248874
 
1.0%
Other values (3367) 21460932
86.4%
ValueCountFrequency (%)
0 3084
 
< 0.1%
0.19 1
 
< 0.1%
0.33 15
 
< 0.1%
0.43 7
 
< 0.1%
0.48 1
 
< 0.1%
0.51 1
 
< 0.1%
0.55 1
 
< 0.1%
0.59 1
 
< 0.1%
0.66 2
 
< 0.1%
0.89 18060
0.1%
ValueCountFrequency (%)
7680 1
 
< 0.1%
6468 2
 
< 0.1%
6100 2
 
< 0.1%
6000 4
 
< 0.1%
5800 9
 
< 0.1%
5500 1
 
< 0.1%
3537.3 24
< 0.1%
2298.84 1
 
< 0.1%
2250 11
< 0.1%
2249.97 1
 
< 0.1%

State Bottle Retail
Real number (ℝ)

SKEWED 

Distinct3846
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.409941
Minimum0
Maximum11520
Zeros3084
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size189.6 MiB
2024-08-10T00:25:45.860404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.44
Q18.31
median12.38
Q318.75
95-th percentile35.22
Maximum11520
Range11520
Interquartile range (IQR)10.44

Descriptive statistics

Standard deviation16.564842
Coefficient of variation (CV)1.0749452
Kurtosis83966.495
Mean15.409941
Median Absolute Deviation (MAD)4.86
Skewness183.03253
Sum3.8289807 × 108
Variance274.39399
MonotonicityNot monotonic
2024-08-10T00:25:46.063512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.38 440414
 
1.8%
11.25 416848
 
1.7%
15 393457
 
1.6%
9.75 357595
 
1.4%
7.5 322103
 
1.3%
12 253615
 
1.0%
5.25 249031
 
1.0%
16.5 246789
 
1.0%
10.5 242173
 
1.0%
13.5 233326
 
0.9%
Other values (3836) 21692120
87.3%
ValueCountFrequency (%)
0 3084
 
< 0.1%
0.29 1
 
< 0.1%
0.5 15
 
< 0.1%
0.65 7
 
< 0.1%
0.72 1
 
< 0.1%
0.76 1
 
< 0.1%
0.83 1
 
< 0.1%
0.88 1
 
< 0.1%
0.99 2
 
< 0.1%
1.34 18060
0.1%
ValueCountFrequency (%)
11520 1
 
< 0.1%
9702 2
 
< 0.1%
9150 2
 
< 0.1%
9000 4
 
< 0.1%
8700 9
 
< 0.1%
8250 1
 
< 0.1%
5305.95 24
< 0.1%
3448.26 1
 
< 0.1%
3375 11
< 0.1%
3374.96 1
 
< 0.1%

Bottles Sold
Real number (ℝ)

SKEWED 

Distinct692
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.636772
Minimum-240
Maximum15000
Zeros9
Zeros (%)< 0.1%
Negative400
Negative (%)< 0.1%
Memory size189.6 MiB
2024-08-10T00:25:46.248033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-240
5-th percentile1
Q13
median6
Q312
95-th percentile24
Maximum15000
Range15240
Interquartile range (IQR)9

Descriptive statistics

Standard deviation29.505095
Coefficient of variation (CV)2.7738768
Kurtosis18435.701
Mean10.636772
Median Absolute Deviation (MAD)4
Skewness65.304444
Sum2.6429699 × 108
Variance870.55063
MonotonicityNot monotonic
2024-08-10T00:25:46.434568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 6570629
26.4%
6 5051881
20.3%
2 3238769
13.0%
1 2771696
11.2%
3 2423744
 
9.8%
24 1429527
 
5.8%
4 1335652
 
5.4%
48 389102
 
1.6%
5 311554
 
1.3%
36 202301
 
0.8%
Other values (682) 1122626
 
4.5%
ValueCountFrequency (%)
-240 1
 
< 0.1%
-156 1
 
< 0.1%
-120 1
 
< 0.1%
-72 2
 
< 0.1%
-60 2
 
< 0.1%
-48 14
< 0.1%
-36 7
 
< 0.1%
-30 4
 
< 0.1%
-24 33
< 0.1%
-18 5
 
< 0.1%
ValueCountFrequency (%)
15000 2
< 0.1%
13200 4
< 0.1%
11952 1
 
< 0.1%
11880 1
 
< 0.1%
11124 1
 
< 0.1%
7920 4
< 0.1%
7632 1
 
< 0.1%
7260 1
 
< 0.1%
7116 1
 
< 0.1%
6750 1
 
< 0.1%

Sale (Dollars)
Real number (ℝ)

SKEWED 

Distinct31523
Distinct (%)0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean140.81338
Minimum-3375
Maximum279557.28
Zeros4943
Zeros (%)< 0.1%
Negative400
Negative (%)< 0.1%
Memory size189.6 MiB
2024-08-10T00:25:46.659150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3375
5-th percentile11.14
Q133.6
median74.28
Q3147.6
95-th percentile342.24
Maximum279557.28
Range282932.28
Interquartile range (IQR)114

Descriptive statistics

Standard deviation489.26328
Coefficient of variation (CV)3.4745511
Kurtosis30136.114
Mean140.81338
Median Absolute Deviation (MAD)49.47
Skewness88.295247
Sum3.4988563 × 109
Variance239378.56
MonotonicityNot monotonic
2024-08-10T00:25:46.835157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 270962
 
1.1%
135 224866
 
0.9%
162 181797
 
0.7%
148.56 181670
 
0.7%
180 177391
 
0.7%
64.8 174545
 
0.7%
45 163792
 
0.7%
126 148000
 
0.6%
94.2 135410
 
0.5%
72 132935
 
0.5%
Other values (31513) 23056103
92.8%
ValueCountFrequency (%)
-3375 1
< 0.1%
-1656 1
< 0.1%
-1439.52 1
< 0.1%
-1394.7 1
< 0.1%
-1348.8 1
< 0.1%
-1170 1
< 0.1%
-1124.4 1
< 0.1%
-1080 1
< 0.1%
-972 1
< 0.1%
-890.64 1
< 0.1%
ValueCountFrequency (%)
279557.28 1
 
< 0.1%
254100 2
< 0.1%
250932 4
< 0.1%
225838.8 1
 
< 0.1%
196004.88 1
 
< 0.1%
181962 1
 
< 0.1%
156807 1
 
< 0.1%
150559.2 4
< 0.1%
145084.32 1
 
< 0.1%
130416 1
 
< 0.1%

Volume Sold (Liters)
Real number (ℝ)

SKEWED 

Distinct1577
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1722439
Minimum-156
Maximum15000
Zeros10
Zeros (%)< 0.1%
Negative400
Negative (%)< 0.1%
Memory size189.6 MiB
2024-08-10T00:25:47.018290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-156
5-th percentile0.5
Q11.5
median4.8
Q310.5
95-th percentile21
Maximum15000
Range15156
Interquartile range (IQR)9

Descriptive statistics

Standard deviation35.311108
Coefficient of variation (CV)3.8497786
Kurtosis12528.506
Mean9.1722439
Median Absolute Deviation (MAD)4.2
Skewness65.3384
Sum2.2790716 × 108
Variance1246.8744
MonotonicityNot monotonic
2024-08-10T00:25:47.194567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 4812300
19.4%
10.5 3207479
12.9%
1.5 2048460
 
8.2%
2.25 1720122
 
6.9%
4.5 1511527
 
6.1%
0.75 1482309
 
6.0%
12 1456459
 
5.9%
3 1147574
 
4.6%
21 492224
 
2.0%
1 454534
 
1.8%
Other values (1567) 6514493
26.2%
ValueCountFrequency (%)
-156 1
 
< 0.1%
-63 2
 
< 0.1%
-60 1
 
< 0.1%
-54 2
 
< 0.1%
-52.5 2
 
< 0.1%
-48 2
 
< 0.1%
-45 1
 
< 0.1%
-36 5
< 0.1%
-31.5 2
 
< 0.1%
-27 2
 
< 0.1%
ValueCountFrequency (%)
15000 2
< 0.1%
13200 4
< 0.1%
11880 1
 
< 0.1%
11812.5 1
 
< 0.1%
11340 1
 
< 0.1%
11124 1
 
< 0.1%
8964 1
 
< 0.1%
8736 1
 
< 0.1%
8505 1
 
< 0.1%
8032.5 1
 
< 0.1%

Volume Sold (Gallons)
Real number (ℝ)

SKEWED 

Distinct1875
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4208059
Minimum-41.21
Maximum3962.58
Zeros465
Zeros (%)< 0.1%
Negative400
Negative (%)< 0.1%
Memory size189.6 MiB
2024-08-10T00:25:47.417448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-41.21
5-th percentile0.13
Q10.4
median1.27
Q32.77
95-th percentile5.55
Maximum3962.58
Range4003.79
Interquartile range (IQR)2.37

Descriptive statistics

Standard deviation9.3282946
Coefficient of variation (CV)3.8533839
Kurtosis12528.031
Mean2.4208059
Median Absolute Deviation (MAD)1.1
Skewness65.336529
Sum60150929
Variance87.01708
MonotonicityNot monotonic
2024-08-10T00:25:47.625647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.77 3207479
 
12.9%
2.38 2745687
 
11.1%
2.37 2066613
 
8.3%
0.59 1720122
 
6.9%
3.17 1456459
 
5.9%
0.4 1279070
 
5.1%
0.79 1147574
 
4.6%
0.2 889135
 
3.6%
1.18 815734
 
3.3%
0.39 769411
 
3.1%
Other values (1865) 8750197
35.2%
ValueCountFrequency (%)
-41.21 1
 
< 0.1%
-16.64 2
 
< 0.1%
-15.85 1
 
< 0.1%
-14.26 2
 
< 0.1%
-13.86 2
 
< 0.1%
-12.68 2
 
< 0.1%
-11.88 1
 
< 0.1%
-9.51 5
< 0.1%
-8.32 2
 
< 0.1%
-7.13 2
 
< 0.1%
ValueCountFrequency (%)
3962.58 2
< 0.1%
3487.07 4
< 0.1%
3138.36 1
 
< 0.1%
3120.53 1
 
< 0.1%
2995.71 1
 
< 0.1%
2938.65 1
 
< 0.1%
2368.03 1
 
< 0.1%
2307.8 1
 
< 0.1%
2246.78 1
 
< 0.1%
2121.96 1
 
< 0.1%

Interactions

2024-08-10T00:21:10.720628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:13:45.789565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:27.843295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:09.633173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:51.891208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:34.361071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:14.495089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:53.384258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:33.700068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:13.591293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:52.951792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:32.661659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:13.990757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:13:49.354129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:31.126505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:13.036222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:55.334140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:37.907087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:17.850452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:56.768279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:37.033649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:16.956769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:56.202720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:35.921878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:17.323084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:13:53.023420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:34.537170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:16.423843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:58.841258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:41.600715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:21.259714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:00.165655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:40.334650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:20.426893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:59.554106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:39.297653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:20.606392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:13:56.712285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:37.987215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:19.899628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:02.346879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:45.233279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:24.664610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:03.397681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:43.625369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:23.945293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:02.856957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:42.587404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:23.642566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:00.014712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:41.537292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:23.556966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:06.047292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:48.317134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:27.664094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:06.859740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:47.113181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:26.964924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:06.285804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:45.641688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:26.758594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:03.288010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:45.113776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:27.197022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:09.744255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:51.422132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:30.587138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:10.339974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:50.581454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:29.989203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:09.728628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:48.736476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:30.066594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:06.980475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:48.611133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:30.634307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:13.243554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:54.970073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:33.984125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:13.520171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:53.887837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:33.474113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:13.086668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:52.068748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:33.349926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:10.752833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:52.075809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:34.066608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:16.805466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:58.435579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:37.360703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:16.813584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:57.108745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:37.012478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:16.379231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:55.361434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:36.400150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:13.989457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:55.642260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:37.754369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:20.505382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:01.658574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:40.409561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:20.338339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:00.591143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:39.935114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:19.806421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:58.412842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:39.732918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:17.645203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:59.096101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:41.252457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:24.008445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:05.209645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:43.864573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:23.647568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:03.892231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:43.386396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:23.004790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:01.737129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:42.795739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:20.928033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:02.581448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:44.675390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:27.587555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:08.350863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:46.905946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:26.922935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:07.185361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:46.447694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:26.342727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:04.653396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:45.716180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:14:24.300781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:06.032240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:15:48.139003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:16:31.140567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:11.484920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:17:49.940642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:18:30.203750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:10.561791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:19:49.520101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:20:29.617341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-10T00:21:07.688010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-08-10T00:21:55.689784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-10T00:22:34.841309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-10T00:24:04.153722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Invoice/Item NumberDateStore NumberStore NameAddressCityZip CodeStore LocationCounty NumberCountyCategoryCategory NameVendor NumberVendor NameItem NumberItem DescriptionPackBottle Volume (ml)State Bottle CostState Bottle RetailBottles SoldSale (Dollars)Volume Sold (Liters)Volume Sold (Gallons)
0INV-1658980003512/27/20182600Hy-Vee Food Store / Oskaloosa110 S D StOskaloosa52577.0POINT (-92.649764 41.295218)62.0MAHASKA1011600.0Straight Rye Whiskies255.0Infinium Spirits27102Templeton 4YR Rye675018.0927.146162.844.501.18
1INV-1666900002912/31/20184843Stewart Road Fast Break2418 Stewart RdMuscatine52761.0POINT (-91.07265200000002 41.390911)70.0MUSCATINE1082200.0Imported Schnapps421.0SAZERAC COMPANY INC69634Dr McGillicuddys Cherry243755.338.00216.000.750.19
2INV-1662400006412/28/20182593Hy-Vee Food Store / Carroll905 US Highway 30 WestCarroll51401.0POINT (-94.853591 42.064155)14.0CARROLL1011300.0Single Barrel Bourbon Whiskies259.0Heaven Hill Brands18120Evan Williams Vintage675015.4723.2112278.529.002.37
3INV-1661040005512/28/20184083Fareway Stores #909 / Ankeny109 SE Oralabor RdAnkeny50021.0POINT (-93.59949600000002 41.702811)77.0POLK1701100.0Temporary & Specialty Packages260.0DIAGEO AMERICAS77903Smirnoff Peppermint Twist127508.2512.38449.523.000.79
4INV-1657530002212/27/20183417Big G Food StorePo Box 261 310 W DillonMarengo52301.0NaN48.0IOWA1011600.0Straight Rye Whiskies255.0Infinium Spirits26781Templeton Rye Rare Cask Strength675029.9944.99144.990.750.19
5INV-1662330000112/28/20184190Fareway Stores #888 / Jefferson1207 N Elm StJefferson50129.0POINT (-94.377667 42.026833)37.0GREENE1081600.0Whiskey Liqueur421.0SAZERAC COMPANY INC64867Fireball Cinnamon Whiskey12100011.3417.0112204.1212.003.17
6INV-1661540004412/28/20182627Hy-Vee Wine & Spirits #1 / MLK3330 Martin Luther King Jr PkwyDes Moines50310.0POINT (-93.65078800000002 41.625924)77.0POLK1031100.0American Vodkas322.0Prestige Wine & Spirits Group36595Kinky Vodka6175012.5018.75356.255.251.38
7INV-1667640006912/31/20184312I-80 Liquor / Council Bluffs2411 S 24TH ST #1Council Bluffs51501.0POINT (-95.8792 41.238092)78.0POTTAWATTA1081400.0American Schnapps65.0Jim Beam Brands82847Dekuyper Luscious Peachtree1210007.8711.8124283.4424.006.34
8INV-1663850003912/29/20183443Super Saver Iv1141 N BroadwayCouncil Bluffs51503.0POINT (-95.836515 41.270824)78.0POTTAWATTA1071100.0Cocktails /RTD395.0PROXIMO591541800 Ultimate Margarita6175010.0415.06230.123.500.92
9INV-1663110003212/29/20182550Hy-Vee Food Store / Osceola510 West MclaneOsceola50213.0POINT (-93.77321 41.030569)20.0CLARKE1031200.0American Flavored Vodka260.0DIAGEO AMERICAS77647Smirnoff Citrus127508.2512.38337.142.250.59
Invoice/Item NumberDateStore NumberStore NameAddressCityZip CodeStore LocationCounty NumberCountyCategoryCategory NameVendor NumberVendor NameItem NumberItem DescriptionPackBottle Volume (ml)State Bottle CostState Bottle RetailBottles SoldSale (Dollars)Volume Sold (Liters)Volume Sold (Gallons)
24847471INV-1659560003012/27/20185335Expo Liquor1810 State StreetBettendorf52722.0POINT (-90.506775 41.525308)82.0SCOTT1022200.0100% Agave Tequila65.0Jim Beam Brands88548Hornitos Plata1275012.5018.75356.252.250.59
24847472INV-1659110001012/27/20185374Iowa Liquor & Tobacco1021 East Main StreetOttumwa52501.0POINT (-92.400704 41.010404)90.0WAPELLO1031100.0American Vodkas434.0LUXCO INC36308Hawkeye Vodka617507.1710.76664.5610.502.77
24847473INV-1661000009612/28/20185425Spirits Liquor109 E 1st St. # BGrimes50111.0POINT (-93.793812 41.68840000000001)77.0POLK1082000.0Imported Cordials & Liqueurs192.0Mast-Jagermeister US, Inc65256Jagermeister Liqueur1275012.4518.68356.042.250.59
24847474INV-1656560001412/26/20184902Broadway Liquor821 Broadway StWaterloo50703.0POINT (-92.345144 42.513772)7.0BLACK HAWK1052100.0Imported Brandies420.0MOET HENNESSY USA48105Hennessy VS1237510.4915.7412188.884.501.18
24847475INV-1659830012712/27/20183820Charlie's Wine and Spirits,507 W 19th StSioux City51103.0POINT (-96.420193 42.510535)97.0WOODBURY1032200.0Imported Flavored Vodka65.0Jim Beam Brands35628Pinnacle Whipped6175012.5918.89237.783.500.92
24847476INV-1667450002712/31/20182650Hy-Vee Wine and Spirits / Harlan1808 23rd StHarlan51537.0POINT (-95.339881 41.650658)83.0SHELBY1012100.0Canadian Whiskies260.0DIAGEO AMERICAS10808Crown Royal Regal Apple12100018.8928.3412340.0812.003.17
24847477INV-1661910004612/28/20183528Super Target T-0804 Mason City3450 4th St SWMason City50401.0POINT (-93.250371 43.148393)17.0CERRO GORD1081600.0Whiskey Liqueur421.0SAZERAC COMPANY INC64866Fireball Cinnamon Whiskey127509.0013.5012162.009.002.37
24847478INV-1658230004512/27/20183622Wal-Mart 1415 / Spirit Lake2600 Hwy 71Spirit Lake51360.0POINT (-95.126535 43.416452)30.0DICKINSON1032200.0Imported Flavored Vodka370.0PERNOD RICARD USA33467Absolut Lime1275011.4917.2412206.889.002.37
24847479INV-1660820003512/28/20184959Bani's2128 College StCedar Falls50613.0POINT (-92.455801 42.518018000000005)7.0BLACK HAWK1062500.0Flavored Rum370.0PERNOD RICARD USA42712Malibu Coconut Rum Mini125004.957.43214.861.000.26
24847480INV-1661960009512/28/20182515Hy-Vee Food Store #1 / Mason City2400 4th St SWMason City50401.0POINT (-93.23551 43.149557)17.0CERRO GORD1022200.0100% Agave Tequila260.0DIAGEO AMERICAS89164Don Julio 1942675064.9997.49197.490.750.19